Automatic apex frame spotting in micro-expression database

Sze-Teng Liong, John See, Kok Sheik Wong, Anh Cat Le Ngo, Yee-Hui Oh, Raphael Phan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

33 Citations (Scopus)

Abstract

Micro-expression usually occurs at high-stakes situations and may provide useful information in the field of behavioral psychology for better interpretion and analysis. Unfortunately, it is technically challenging to detect and recognize micro-expressions due to its brief duration and the subtle facial distortions. Apex frame, which is the instant indicating the most expressive emotional state in a video, is effective to classify the emotion in that particular frame. In this work, we present a novel method to spot the apex frame of a spontaneous micro-expression video sequence. A binary search approach is employed to locate the index of the frame in which the peak facial changes occur. Features from specific facial regions are extracted to better represent and describe the expression details. The defined facial regions are selected based on the action unit and landmark coordinates of the subject, in which case these processes are automated. We consider three distinct feature descriptors to evaluate the reliability of the proposed approach. Improvements of at least 20% are achieved when compared to the baselines.

Original languageEnglish
Title of host publication2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)
PublisherIEEE
Pages665-669
Number of pages5
ISBN (Electronic)9781479961009
DOIs
Publication statusPublished - 9 Jun 2016
Event3rd IAPR Asian Conference on Pattern Recognition 2015 - Kuala Lumpur, Malaysia
Duration: 3 Nov 20166 Nov 2016

Conference

Conference3rd IAPR Asian Conference on Pattern Recognition 2015
Abbreviated titleACPR 2015
Country/TerritoryMalaysia
CityKuala Lumpur
Period3/11/166/11/16

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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